#!/usr/bin/python3.6
# -*- coding: utf-8 -*-
# @Time    : 2019-05-23 14:11
# @Author  : Yongfei Liu
# @Email   : liuyf3@shanghaitech.edu.cn


from maskrcnn_benchmark.config import cfg
import torchvision.models as models
import torch
import torch.nn as nn
import numpy as np
import os.path as osp


import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo


__all__ = ['ResNet', 'resnet101']


model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}


def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)
        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(self, block, layers, cfg, num_classes=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=cfg.MODEL.BACKBONE.LAST_LAYER_STRIDE)
        self.avgpool = nn.AvgPool2d(7, stride=1)
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x


def resnet101(pretrained=False, cfg=None):
    """Constructs a ResNet-101 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3], cfg=cfg)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
    return model


class ResNetC3(nn.Module):
    def __init__(self, cfg=None):
        super(ResNetC3, self).__init__()
        self.cfg = cfg
        self._init_modules()

    def _init_modules(self):
        resnet = resnet101(pretrained=False, cfg=self.cfg)
        self.RCNN_base = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool,
                                       resnet.layer1, resnet.layer2, resnet.layer3)
        self.RCNN_top = nn.Sequential(resnet.layer4)

    def forward(self, input):
        output = self.RCNN_base(input)
        output_list = [output]
        return tuple(output_list)


class ResNetC4Top(nn.Module):
    def __init__(self, cfg=None):
        super(ResNetC4Top, self).__init__()
        self.cfg = cfg
        self._init_modules()

    def _init_modules(self):
        resnet = resnet101(pretrained=False, cfg=self.cfg)
        self.RCNN_base = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool,
                                       resnet.layer1, resnet.layer2, resnet.layer3)

        self.RCNN_top = nn.Sequential(resnet.layer4)

    def forward(self, input):
        output = self.RCNN_base(input)
        output_list = [output]
        return tuple(output_list)


class ResNetC4(nn.Module):
    def __init__(self, cfg=None):
        super(ResNetC4, self).__init__()
        self.cfg = cfg
        self._init_modules()


    def _init_modules(self):
        resnet = resnet101(pretrained=False, cfg=self.cfg)
        self.RCNN_base = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool,
                                       resnet.layer1, resnet.layer2, resnet.layer3)

        self.RCNN_top = nn.Sequential(resnet.layer4)

    def forward(self, input):

        output = self.RCNN_base(input)
        output = self.RCNN_top(output)
        output_list = [output]
        return tuple(output_list)


class ResNetC4FPNOUT(nn.Module):
    def __init__(self, cfg=None):
        super(ResNetC4FPNOUT, self).__init__()
        self.cfg = cfg
        self._init_modules()

    def _init_modules(self):
        resnet = resnet101(pretrained=False, cfg=self.cfg)
        self.RCNN_base = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool,
                                       resnet.layer1, resnet.layer2, resnet.layer3)

        self.RCNN_top = nn.Sequential(resnet.layer4)

    def forward(self, input):

        x0 = self.RCNN_base[0](input)
        x1 = self.RCNN_base[1](x0)
        x2 = self.RCNN_base[2](x1)
        x3 = self.RCNN_base[3](x2)
        x4 = self.RCNN_base[4](x3)
        x5 = self.RCNN_base[5](x4)
        x6 = self.RCNN_base[6](x5)
        output = self.RCNN_top(x6)

        output_list = [x4, x5, x6, output]
        return output_list




